Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

Leila Ismail, Huned Materwala

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages527-533
Number of pages7
ISBN (Electronic)9781728176246
DOIs
Publication statusPublished - Dec 2020
Event2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States
Duration: Dec 16 2020Dec 18 2020

Publication series

NameProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conference

Conference2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/TerritoryUnited States
CityLas Vegas
Period12/16/2012/18/20

Keywords

  • artificial intelligence
  • classification models
  • diabetes mellitus type 2
  • health informatics
  • machine learning models

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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